A robust iterative super-resolution mosaicking algorithm using an adaptive and directional Huber-Markov regularization

Ghosh, Debabrata; Kaabouch, Naima; Hu, Wen‐Chen

doi:10.1016/j.jvcir.2016.06.008

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…Image stitching or image mosaicking involves obtaining a wider field of view of a scene from a sequence of partial views [ 25 ]. Image stitching deals with low overlapping images and requires accurate alignment at the pixel level to avoid visual discontinuities.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Image Stitching Method for Dual-Sensor Inspection

Shahsavarani,

Lopez,

Ibarra-Castanedo

et al. 2024

Sensors

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Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Advanced Image Stitching Method for Dual-Sensor Inspection

Shahsavarani,

Lopez,

Ibarra-Castanedo

et al. 2024

Sensors

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“…Otherwise, to treat nonparametric deformations between the LR images, an elastic registration was proposed in [26], but it is specified to small deformations and does not tackle larger ones. Other techniques use the maximum a posteriori (MAP) [2,17,38] with accurate spatial domain observation model to reconstruct the HR image. More recently, other methods were proposed [5] but still suffering from the misregistration errors.…”

Section: Introductionmentioning

confidence: 99%

An improved PDE-constrained optimization fluid registration for image multi-frame super resolution

et al. 2022

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The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on a partial differential equation (PDE)-constrained optimization fluid image registration and we use a fourth order PDE to treat both the registration and restoration steps that guarantee the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.

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“…This procedure employed Barzilai-Borwein gradient descent and locally adaptive majorize-minimization, in low-and highdimensional settings, respectively. Ghosh et al [17] proposed a super-resolution algorithm using a Huber norm-based maximum likelihood estimation by combining with an adaptive directional Huber-Markov regularization. This algorithm is simple and can obtain solutions but the wide-angle image with high resolution are required.…”

Section: Introductionmentioning

confidence: 99%

Robust Fused Lasso Penalized Huber Regression with Nonasymptotic Property and Implementation Studies

Xin¹,

Xie²,

Xiao³

2022

Preprint

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For some special data in reality, such as the genetic data, adjacent genes may have the similar function. Thus ensuring the smoothness between adjacent genes is highly necessary. But, in this case, the standard lasso penalty just doesn't seem appropriate anymore. On the other hand, in high-dimensional statistics, some datasets are easily contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address both issues, in this paper, we propose an adaptive Huber regression for robust estimation and inference, in which, the fused lasso penalty is used to encourage the sparsity of the coefficients as well as the sparsity of their differences, i.e., local constancy of the coefficient profile. Theoretically, we establish its nonasymptotic estimation error bounds under 2-norm in high-dimensional setting. The proposed estimation method is formulated as a convex, nonsmooth and separable optimization problem, hence, the alternating direction method of multipliers can be employed. In the end, we perform on simulation studies and real cancer data studies, which illustrate that the proposed estimation method is more robust and predictive.

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A robust iterative super-resolution mosaicking algorithm using an adaptive and directional Huber-Markov regularization

Cited by 13 publications

References 38 publications

Advanced Image Stitching Method for Dual-Sensor Inspection

Advanced Image Stitching Method for Dual-Sensor Inspection

An improved PDE-constrained optimization fluid registration for image multi-frame super resolution

Robust Fused Lasso Penalized Huber Regression with Nonasymptotic Property and Implementation Studies

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